A novel Gabor convolution and self-attention mechanism for automated and precise Alzheimer’s disease detection in MRI using adaptive explainable ShuffleNetV2 framework

Abstract Alzheimer’s disease (AD) is a progressive brain disorder that is caused by the nerve cells and results in memory loss and cognitive decline, which highly impacts daily function. In the present times, several artificial intelligence (AI)-based models, such as deep learning strategies, are utilized to automatically analyse the MRI and detect AD. Various researchers integrate explainable AI (XAI) into deep learning to advance the detection process and to understand the reasons behind the outcome to support clinical settings. These XAI-based frameworks are highly efficient and able to evaluate the subtle changes and complex patterns in the MRI. In addition, these types of advanced models improve the diagnosis process and provide early diagnosis results to enhance the patient’s prognosis. Thus, this work develops an enhanced AD detection model by using advanced XAI approaches. Initially, the MRIs are gathered from the standard datasets and fed into Gabor Convolution with Channel Self-Attention-based Adaptive Explainable ShuffleNetV2 (GC-CSA-AXAI) to detect the presence and absence of AD. It uses the Gabor convolution, which aids in capturing the fine-grained textures, along with the edges that are present in the given images, while the channel self-attention allows the developed GC-CSA-AXAI to provide more intense concentration only on the essential brain regions. Moreover, the ShuffleNetV2 is the core network, which provides optimal results by ensuring that the network is rapid and suitable for clinical applications, while the explainable nature of the framework helps to build crucial trust and transparency in the automated process. Finally, to enhance the performance, an adaptive component is used, which applies the Reformulated Arbitrary Term of Puma Optimizer (RATPO) to fine-tune the critical parameters, resulting in quicker and more accurate results. Finally, the developed model offers the detected outcome, and then, the efficiency is validated by differentiating the introduced approach from several earlier models and showing its improved functionality over others.